As today's organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages.
Presenting the contributions of leading experts in their respective fields, Big Data: Algorithms, Analytics, and Applications bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields, such as medicine, science, and engineering. The book is organized into five main sections:
Big Data Management-considers the research issues related to the management of Big Data, including indexing and scalability aspects
Big Data Processing-addresses the problem of processing Big Data across a wide range of resource-intensive computational settings
Big Data Stream Techniques and Algorithms-explores research issues regarding the management and mining of Big Data in streaming environments
Big Data Privacy-focuses on models, techniques, and algorithms for preserving Big Data privacy
Big Data Applications-illustrates practical applications of Big Data across several domains, including finance, multimedia tools, biometrics, and satellite Big Data processing
Overall, the book reports on state-of-the-art studies and achievements in algorithms, analytics, and applications of Big Data. It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. It also explores related applications in diverse sectors, covering technologies for media/data communication, elastic media/data storage, cross-network media/data fusion, and SaaS.
Rezensionen / Stimmen
The collection presented in the book covers fundamental and realistic issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields. ... This book is required understanding for anyone working in a major field of science, engineering, business, and financing.-Jack Dongarra, University of Tennessee
The editors have assembled an impressive book consisting of 22 chapters written by 57 authors from 12 countries across America, Europe, and Asia. ... This book has great potential to provide fundamental insight and privacy to individuals, long-lasting value to organizations, and security and sustainability to the cyber-physical-social ecosystem ....-D. Frank Hsu, Fordham University
These editors are active researchers and have done a lot of work in the area of Big Data. They assembled a group of outstanding chapter authors. ... Each section contains several case studies to demonstrate how the related issues are addressed. ... I highly recommend this timely and valuable book. I believe that it will benefit many readers and contribute to the further development of Big Data research.-Dr. Yi Pan, Georgia State University
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Researchers in data mining, machine learning, business analytics, and computational science.
Illustrationen
155 s/w Abbildungen, 15 s/w Tabellen
15 Tables, black and white; 155 Illustrations, black and white
Maße
Höhe: 260 mm
Breite: 183 mm
Dicke: 31 mm
Gewicht
ISBN-13
978-1-4822-4055-9 (9781482240559)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Kuan-Ching Li, Hai Jiang, Laurence T. Yang, Alfredo Cuzzocrea
Big Data Management. Big Data Processing. Big Data Stream Techniques and Algorithms. Big Data Privacy. Big Data Applications.
